The advantage of using \(D_\phi \)  as the reward function is that, since it is updated by the adversarial loss at every iteration, it improves its capability of distinguishing between real and fake. Due to this, it can provide better feedback to \(G_\theta \) . The gradient of the loss function with respect to \(\theta \)  from [1]} is given by:
